





"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
       from transformers.models.mobilevitva.modeling_mobilevitva import (
           MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
           make_divisible,
       )


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor

class 						lowercase   (   _UpperCAmelCase   ):





      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase    		=			self.config_class(**self.inputs_dict					)
            self.parent.assertTrue(hasattr(lowercase ,		"""width_multiplier"""					)					)







class 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=13 ,		lowercase=64 ,		lowercase=2 ,		lowercase=3 ,		lowercase="swish" ,		lowercase=3 ,		lowercase=32 ,		lowercase=0.1 ,		lowercase=0.02 ,		lowercase=True ,		lowercase=True ,		lowercase=10 ,		lowercase=None ,		lowercase=0.25 ,		lowercase=0.0 ,		lowercase=0.0 ,		)						->	Dict:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			image_size
            lowerCAmelCase    		=			patch_size
            lowerCAmelCase    		=			num_channels
            lowerCAmelCase    		=			make_divisible(512 * width_multiplier ,		divisor=8					)
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			conv_kernel_size
            lowerCAmelCase    		=			output_stride
            lowerCAmelCase    		=			classifier_dropout_prob
            lowerCAmelCase    		=			use_labels
            lowerCAmelCase    		=			is_training
            lowerCAmelCase    		=			num_labels
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			scope
            lowerCAmelCase    		=			width_multiplier
            lowerCAmelCase    		=			ffn_dropout
            lowerCAmelCase    		=			attn_dropout





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			None
            if self.use_labels:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.num_labels					)
                  lowerCAmelCase    		=			ids_tensor([self.batch_size, self.image_size, self.image_size] ,		self.num_labels					)

            lowerCAmelCase    		=			self.get_config()

            return config, pixel_values, labels, pixel_labels





      def        _snake_case     (			self					)						->	Dict:
            return MobileViTVaConfig(
                image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		hidden_act=self.hidden_act ,		conv_kernel_size=self.conv_kernel_size ,		output_stride=self.output_stride ,		classifier_dropout_prob=self.classifier_dropout_prob ,		initializer_range=self.initializer_range ,		width_multiplier=self.width_multiplier ,		ffn_dropout=self.ffn_dropout_prob ,		attn_dropout=self.attn_dropout_prob ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			MobileViTVaModel(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(
                result.last_hidden_state.shape ,		(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			self.num_labels
            lowerCAmelCase    		=			MobileViTVaForImageClassification(lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase ,		labels=lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	str:
            lowerCAmelCase    		=			self.num_labels
            lowerCAmelCase    		=			MobileViTVaForSemanticSegmentation(lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(
                result.logits.shape ,		(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,		)
            lowerCAmelCase    		=			model(lowercase ,		labels=lowercase					)
            self.parent.assertEqual(
                result.logits.shape ,		(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,		)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			config_and_inputs
            lowerCAmelCase    		=			{"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 						lowercase   (   _UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      _SCREAMING_SNAKE_CASE							      = (
          {
              'feature-extraction': MobileViTVaModel,
              'image-classification': MobileViTVaForImageClassification,
              'image-segmentation': MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			MobileViTVaModelTester(self					)
            lowerCAmelCase    		=			MobileViTVaConfigTester(self ,		config_class=lowercase ,		has_text_modality=lowercase					)





      def        _snake_case     (			self					)						->	Optional[Any]:
            self.config_tester.run_common_tests()





      @unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""					)
      def        _snake_case     (			self					)						->	int:
            pass





      @unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""					)
      def        _snake_case     (			self					)						->	List[Any]:
            pass





      @unittest.skip(reason="""MobileViTV2 does not output attentions"""					)
      def        _snake_case     (			self					)						->	Union[str, Any]:
            pass





      @require_torch_multi_gpu
      @unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""					)
      def        _snake_case     (			self					)						->	List[str]:
            pass





      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
      def        _snake_case     (			self					)						->	List[str]:
            pass





      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  lowerCAmelCase    		=			inspect.signature(model.forward					)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowerCAmelCase    		=			[*signature.parameters.keys()]

                  lowerCAmelCase    		=			["""pixel_values"""]
                  self.assertListEqual(arg_names[:1] ,		lowercase					)





      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            def check_hidden_states_output(lowercase ,		lowercase ,		lowercase					):
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()

                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

                  lowerCAmelCase    		=			outputs.hidden_states

                  lowerCAmelCase    		=			5
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                  # with the width and height being successively divided by 2.
                  lowerCAmelCase    		=			2
                  for i in range(len(lowercase					)					):
                        self.assertListEqual(
                            list(hidden_states[i].shape[-2:]					) ,		[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,		)
                        divisor *= 2

                  self.assertEqual(self.model_tester.output_stride ,		divisor // 2					)

            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  check_hidden_states_output(lowercase ,		lowercase ,		lowercase					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True

                  check_hidden_states_output(lowercase ,		lowercase ,		lowercase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowercase					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*lowercase					)





      @slow
      def        _snake_case     (			self					)						->	int:
            for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase    		=			MobileViTVaModel.from_pretrained(lowercase					)
                  self.assertIsNotNone(lowercase					)







def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image






@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @cached_property
      def        _snake_case     (			self					)						->	str:
            return (
                MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""					)
                if is_vision_available()
                else None
            )





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""					).to(
                lowercase					)

            lowerCAmelCase    		=			self.default_image_processor
            lowerCAmelCase    		=			prepare_img()
            lowerCAmelCase    		=			image_processor(images=lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)

            # verify the logits
            lowerCAmelCase    		=			torch.Size((1, 1_000)					)
            self.assertEqual(outputs.logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]					).to(lowercase					)

            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		lowercase ,		atol=1e-4					)					)





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)
            lowerCAmelCase    		=			model.to(lowercase					)

            lowerCAmelCase    		=			MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)

            lowerCAmelCase    		=			prepare_img()
            lowerCAmelCase    		=			image_processor(images=lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
            lowerCAmelCase    		=			outputs.logits

            # verify the logits
            lowerCAmelCase    		=			torch.Size((1, 21, 32, 32)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [
                    [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                    [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                    [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
                ] ,		device=lowercase ,		)

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,		lowercase ,		atol=1e-4					)					)





      @slow
      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase    		=			MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)
            lowerCAmelCase    		=			model.to(lowercase					)

            lowerCAmelCase    		=			MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""					)

            lowerCAmelCase    		=			prepare_img()
            lowerCAmelCase    		=			image_processor(images=lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)

            lowerCAmelCase    		=			outputs.logits.detach().cpu()

            lowerCAmelCase    		=			image_processor.post_process_semantic_segmentation(outputs=lowercase ,		target_sizes=[(50, 60)]					)
            lowerCAmelCase    		=			torch.Size((50, 60)					)
            self.assertEqual(segmentation[0].shape ,		lowercase					)

            lowerCAmelCase    		=			image_processor.post_process_semantic_segmentation(outputs=lowercase					)
            lowerCAmelCase    		=			torch.Size((32, 32)					)
            self.assertEqual(segmentation[0].shape ,		lowercase					)













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






